New & Noteworthy

SGD now provides links from individual S. cerevisiae genes to their Schizosaccharomyces pombe orthologs at PomBase. These links are labeled “PomBase”, and can be found on the Locus Summary Pages, within the Homologs section.

Just like the chicken or milk you buy at a store, chromosomes have a shelf life too. Of course, chromosomes don’t spoil because of growing bacteria. Instead, they go bad because they lose a little of the telomeres at their ends each time they are copied. Once these telomeres get too short, the chromosome stops working and the cell dies.

You can make this chicken last longer by freezing it. You can do the same for a chromosome in yeast with a shot of alcohol. Image from Food & Spirits Magazine via Wikimedia Commons

Turns out food and chromosomes have another thing in common—the rates of spoilage of both can be affected by their environment. For example, we all know that chicken will last longer if you store it in a refrigerator and that it will go bad sooner if you leave it out on the counter on a hot day. In a new study out in PLoS Genetics, Romano and coworkers show a variety of ways that the loss of telomeres can be slowed down or sped up in the yeast S. cerevisiae. And importantly, they also show that some forms of environmental stress have no effect.

The authors looked at the effect of thirteen different environments on telomere length over 100-400 generations. They found that caffeine, high temperature and low levels of hydroxyurea lead to shortened telomeres, while alcohol and acetic acid lead to longer telomeres. It seems that for a long life, yeast should lay off the espresso and and try to avoid fevers, while enjoying those martinis and sauerbraten.

Romano and coworkers also found a number of conditions that had no effect on telomere length, with the most significant being oxidative stress. In contrast, previous studies in humans had suggested that the oxidative stress associated with emotional stress contributed to increased telomere loss; given these results, this may need to be looked at again. In any event, yeast can deal with the stresses of modern life with little or no impact on their telomere length.

The authors next set out to identify the genes that are impacted by these stressors. They focused on four different conditions—two that led to decreased telomere length, high temperature and caffeine, one that led to longer telomeres, ethanol, and one that had no effect, hydrogen peroxide. As a first step they identified key genes by comparing genome-wide transcript levels under each condition. They then went on to look at the effect of each stressor on strains deleted for each of the genes they identified.

Not surprisingly, the most important genes were those involved with the enzyme telomerase. This enzyme is responsible for adding to the telomeres at the ends of chromosomes. Without something like this, eukaryotes, with their linear chromosomes, would have disappeared long ago.

A key gene they identified was RIF1, encoding a negative regulator of telomerase. Deleting this gene led to decreased effects of ethanol and caffeine, suggesting that this gene is key to each stressor’s effects. The same was not true of high temperature—the strain deleted for RIF1 responded normally to high temperature. So high temperature works through a different mechanism.

Digging deeper into this pathway, Romano and coworkers found that Rap1p was the central player in ethanol’s ability to lengthen telomeres. This makes sense, as the ability of Rif1p to negatively regulate telomerase depends upon its interaction with Rap1p.

The increase in telomere length by ethanol was not just dependent on genes associated with telomerase either. The authors identified a number of other genes involved, including DOA4, SNF7, and DID4.

Caffeine, like ethanol, affected telomere length through Rif1p-Rap1p but with an opposite effect. As caffeine is known to be an inhibitor of phosphatydylinositol-3 kinase related kinases, the authors looked at whether known kinases in the telomerase pathway were involved in caffeine-dependent telomere shortening. They found that when they deleted both TEL1 and MEC1, caffeine no longer affected telomere length.

The authors were not so lucky in their attempts to tease out the mechanism of the ability of high temperature to shorten telomeres. They were not able to identify any single deletions that eliminated this effect of high temperature.

Whatever the mechanisms, the results presented in this study are important for a couple of different reasons. First off, they obviously teach us more about how telomere length is maintained. But this is more than a dry, academic finding.

Given that many of the 400 or so genes involved in maintaining telomere length are evolutionarily conserved, these results may also translate to humans too. This matters because telomere length is involved in a number of diseases and aging.

Studies like this may help us identify novel genes to target in diseases like cancer. And they may help us better understand how lifestyle choices can affect your telomeres and so your health. So if you have a cup of coffee, be sure to spike it with alcohol!

SGD periodically sends out its newsletter to colleagues designated as contacts in SGD. This Winter 2013 newsletter is also available on the community wiki. If you would like to receive the SGD newsletter in the future please use the Colleague Submission/Update form to let us know.

The most interesting board games can’t be played right out of the box. You can admire the board and the game pieces, but before the fun can begin you need to spend some time reading the instructions and understanding the strategy.

A little effort put into learning the game allows you to not only play it, but master it. The same can be said for Gene Ontology! Image by Arbitrarily0 from Wikimedia Commons

Gene Ontology (GO) annotations are a little bit like that. You can get interesting information very quickly by just reading the GO terms on the Locus Summary page of your favorite yeast protein in SGD. But if you look deeper and learn just a little bit more about GO, you’ll find that you can get so much more out of it.

If you’re a molecular or cell biologist, a geneticist, or a computational biologist (or are studying one of those fields), you’re probably already aware of GO. But still, you may be wondering, “Where did these annotations come from? What do those three-letter acronyms mean? How can this help me in my research?” This short and sweet article is a great place to start getting answers to these questions.

We recommend that everyone devote a few minutes to reading this brief article, even if you think you already understand GO. Based on the most frequent questions that we get from researchers who use GO annotations at SGD, we can distill it even further into these top three points as seen from an SGD perspective.

There are people behind these annotations. GO terms are assigned either by real, live humans called biocurators, or computationally using automated methods (each annotation is marked, so you can easily see which is which). At SGD, biocurators are Ph.D. biologists who read the yeast literature and capture experimental results as GO annotations; SGD biocurators are also involved in developing the structure of the GO. We try our best, but like all human beings, we are not infallible. So if you see an annotation that looks wrong or confusing, or if you think an area of the GO could better represent the biology, please contact us (sgd-helpdesk@lists.stanford.edu) to talk about it. The more expert help we can get, the better the GO and our GO annotations will be.

The details matter. Those three-letter codes that accompany each annotation mean something. Imagine you are deciding how to allocate your lab’s resources and a critical experiment will be based on a particular protein having a particular function. You see a GO annotation for that function and that protein, so you’re good to go! But wait a minute…

Those codes tell you the experimental evidence behind the assignment of a GO term to a gene product. If that annotation has an IDA (Inferred from Direct Assay) evidence code, then the function was shown in an actual experiment, so you probably are good to go. On the other hand, if the annotation has an ISS (Inferred from Sequence Similarity) evidence code, then it was made solely based on resemblance to another protein. This is still valuable information, but you might not want to bet the farm (or the lab) on it.

Dates are very important too. Both the annotations and the GO itself are constantly updated to keep up with new biological knowledge. Because of this, everything related to GO – from a single annotation shown on an SGD GO Details page, to the downloadable files that contain all GO annotations or the ontology itself – is associated with the date it was created. So if you do any analysis using GO annotations it’s important to note the dates of both the annotation and ontology files that you used. This is especially important if you repeat a GO term enrichment for a gene set over time. The results will definitely change, as significant enrichments become more strongly supported while marginally significant enrichments may not be reproduced.

Go deeper. GO is not just a list of terms. GO terms have defined relationships to each other, with some being broader (parent terms) and some more specific (child terms). If you really understand the structure of GO, you’ll be able to make much better use of the annotations.

For example, if you look for gene products in SGD annotated to the GO term “mitochondrion,” you’ll currently find 1055 of them1. Does that mean that there are exactly 1055 proteins or noncoding RNAs known to be in yeast mitochondria? Noooo!

There are more than that, because the term “mitochondrion” has more specific child terms such as “mitochondrial matrix”; some proteins are annotated directly to those terms and not to the parent term. If you had used the original list of proteins annotated to “mitochondrion”, you’d be missing 92 gene products2 that are so well-studied that their precise locations in the organelle are known! The structure of the GO allows you to gather all the gene products annotated to a term and to all its child terms (YeastMine has a template tailored to this kind of query).

As you can tell, there is a lot more to GO annotations than a lot of people think. And as you dig deeper, you begin to be able to use them in ever more sophisticated ways. Sort of like the natural progression with a strategy board game like Settlers of Catan. At first, even after reading the instructions, you are just trying to work through the game. But as you play more and more, you quickly learn where to build your roads, which islands to colonize and so much more. So get out there and master GO. You’ll be glad you did.

1As of December 2013, using YeastMine template “GO Term -> All genes” (includes Manually curated and High-throughput annotation types).

2As of December 2013, using YeastMine template “GO Term Name [and children of this term] -> All genes” (filtered to exclude Computational annotation type so that only Manually curated and High-throughput annotation types are included).

Even after all these years of studying the mating response in yeast, there is still more to be learned! Image courtesy of Lori B. Huberman and GENETICS

Our friend Saccharomyces cerevisiae has it pretty easy when it comes to sex. There is no club scene or online dating. Pretty much if an a and an α are close enough together, odds are that they will shmoo towards each other and fuse to create a diploid cell. No fuss, no muss.

Of course there aren’t any visual cues that indicate whether a yeast is a or α. Instead yeast relies on detecting gender-specific pheromones each cell puts out. The a yeast makes a pheromone and an α pheromone receptor, and the α yeast makes α pheromone and an a pheromone receptor. The way yeast finds a hottie is by looking for the yeast of the opposite sex that puts out the most pheromone.

This simple system is similar to ours in that gender is determined by gender specific gene expression. In humans this happens through the amounts of certain hormones that are made. For example, males make a lot of testosterone which turns on the androgen receptor (AR) which then turns a bunch of genes up or down. Both men and women have AR; men just make more testosterone, which causes it to be more active.

Yeast are simpler in that their mating loci encode transcription factors and cofactors that directly regulate a-specific and α-specific genes. Still, in both yeast and human, gender is determined by which genes are on and which are off.

Given how simple the yeast system is and how extensively it has been studied, you might think there is nothing else to learn about yeast mating. You’d be wrong. In a new study out in GENETICS, Huberman and Murray found that a gene with a previously unknown function, YLR040C, is involved in mating. They renamed this gene AFB1 (a-Factor Barrier) since it seems to interfere with a-factor secretion.

The way they found this gene was by creating, as they termed them, transvestite yeast that “pretended” to be the opposite mating type. One strain that they named the MATα-playing-a strain was α but produced a-specific mating proteins, while the other, the MATa-playing-α strain, was a but produced α-specific mating proteins. Sounds easy but it took a bit of genetic engineering to pull off.

The first steps in making the MATa-playing-α strain were to replace STE2 with STE3, MFA1 with MFα1, and MFA2 with MFα2. In addition, they had to delete BAR1 to keep it from chewing up any α factor that got made, and ASG7, which inhibits signaling from STE3. This strain still had the MATa locus, which meant that except for the manipulated genes, it still maintained an a-specific gene expression pattern.

Making the MATα-playing-a strain wasn’t much simpler. They had to replace STE3 with STE2, MFα1 with MFA1, and MFα2 with MFA2. In addition, they drove expression of BAR1 with the haploid specific FUS1 promoter and expression of the a-factor transporter STE6 with the MFα1 promoter. Maybe yeast isn’t so simple after all!

When Huberman and Murray mated the two transvestite strains to each other, they found that while these strains could produce diploid offspring, they weren’t very good at it. In fact, they were about 700-fold worse than true a and α strains! So what’s wrong?

To tease this out the researchers mated each transvestite to a wild type strain. They found that when they mated a wild type a strain to a MATa-playing-α strain, the transvestite’s mating efficiency was only down about three fold. By overexpressing α factor they quickly found that the transvestite strain’s major problem was that it simply didn’t make enough α pheromone. They hypothesized that perhaps differences in promoter strength or in the translation or processing of α-factor were to blame.

The reason for the low mating efficiency of the MATα-playing-a strain, however, wasn’t so simple. When Huberman and Murray mated the MATα-playing-a strain with an α cell, they found it was about 60-fold worse at mating. The first thing they looked for was how much a-factor this strain was producing. Because a-factor is difficult to assay biochemically, they used a novel bioassay instead and found that it secreted much less a-factor than did the wild type a strain. Further investigation showed that the transvestite strain produced something that blocked the ability of a-factor to be secreted.

By comparing the transcriptomes of MATa and MATα-playing-a cells they were able to identify YLR040C as their potential a-factor blocker. They went on to show that when this gene was present, a-factor secretion was indeed inhibited. They hypothesize that their newly named AFB1 may produce a protein that binds to and sequesters a-factor. It may be to a cells what BAR1 is to α cells, helping the yeast cell to sense the pheromone gradient and choose a mating partner.

When Huberman and Murray knocked AFB1 out of the MATα-playing-a strain, it now mated with a wild type α strain about five fold better than before. A nice increase, but it doesn’t completely correct the 60-fold reduction in this transvestite’s mating efficiency. Something else must be going on.

That something appears to be that the strain only arrests for a short time when it encounters α-factor. This would definitely impact mating efficiency, as it is very important that when a and α strains fuse they both be in the same part of the cell cycle. Pheromones usually stop the cell cycle in its tracks, but α-factor can’t seem to keep the MATα-playing-a cell arrested for very long. The researchers looked for genes involved in this transient arrest, but were not able to find any one gene that was responsible.

From all of this the authors conclude that there is a pheromone arms race raging in the yeast world. The most attractive yeast are those that make the most pheromone, so evolution favors higher and higher pheromone production. Just as people on the dating scene need to see past the makeup and trendy clothes to figure out who’s really the best partner, yeast need genes like BAR1 and AFB1 to parse out who is the best mate amid the ever increasing haze of pheromones.

Transcriptional regulation data are now available on new “Regulation” tab pages for virtually every yeast gene. We are collaborating with the YEASTRACT database to display regulation annotations curated both by SGD and by YEASTRACT on these new pages. Regulation annotations are each derived from a published reference, and include a transcriptional regulator, a target gene, the experimental method used to determine the regulatory relationship, and additional data such as the strain background or experimental conditions. The relationships between regulators and the target gene are also depicted in an interactive Network Visualization diagram. The Regulation tab for DNA-binding transcription factors (TFs) includes these items and additionally contains a Regulation Summary paragraph summarizing the regulatory role of that TF, a table listing its protein domains and motifs, DNA binding site information, a table of its regulatory target genes, and an enrichment of the GO Process terms to which its target genes are annotated (view an example). In the coming months we will be adding this extra information to the Regulation pages of other classes of TFs, such as those that act by binding other TFs.

We have also completely redesigned the web display of the Interactions and Literature tab pages, which now include graphical display of data, sortable tables, interactive visualizations, and more navigation options. These pages provide seamless access to other tools at SGD such as GO tools and YeastMine. Please feel free to explore all of these new features from your favorite Locus Summary page and send us your feedback.

Stanford offers an innovative class, targeted at sophomore undergraduates, where students use yeast to determine how a mutation in the p53 gene affects the activity of the resulting p53 protein. What makes this class even cooler is that the p53 mutants come from actual human tumors—the undergraduates are figuring out what actual cancer mutations are doing! And the class uses what we think is the most important organism in the world, S. cerevisiae.

To learn more about the course, we decided to interview Jamie Imam, one of the instructors. After reading the interview, you will almost certainly be as excited about this class as we were and it may even get you to wishing that you could teach the class at your institution. With a little help, you can.

The creators of the course, Tim Stearns and Martha Cyert, really want as many people as possible to use this class to teach undergraduates about what real science is and how fun and exciting it can be. To that end, they are happy to help you replicate the course wherever you are. If you are interested, please contact Tim and/or Martha. You’ll be happy you did. Their contact information can be found at the Stearns lab and Cyert lab websites.

Here now is the interview with Jamie. What a great way to get undergraduates excited about the scientific process.

Dr. Jamie Imam

Can you describe the class?

Sure. Bio44X is designed to be similar to an authentic research experience or as close to one as you can replicate in the classroom. During the quarter, students study mutant versions of a gene called p53, a tumor suppressor that is frequently mutated in cancer. Each partner pair in a classroom gets one p53 mutant that has been identified in a human tumor to study in our yeast system. Throughout the course of the 10 weeks, the students study the transactivation ability of their mutant compared to the wild-type version, and then work to figure out what exactly is wrong with the mutant (Can it bind DNA?, Does it localize to the nucleus properly?, etc.). Multiple sections of this course are taught during the Fall and Winter quarters, so several pairs end up studying the same mutant. We bring these students together to discuss and combine their data throughout the quarter, so there is a lot of collaboration involved. I think the students really enjoy having one topic to study in depth over the quarter rather than short individual modules, and the fact that we are studying a gene so important in cancer makes it easier to get them to care about the work they are doing.

Tell me a little bit about how this class was started.

Previously, Bio44X at Stanford was the more traditional “cookbook” type lab course. Every 2 weeks, the topic would change and students would work through set protocols that had a known correct answer. In 2010, Professors Martha Cyert and Tim Stearns set out to design and pilot a research-based course on a medically relevant topic (the tumor suppressor p53) in response to some national calls for biology lab course reform. Two years and many changes later, the new research-based lab course replaced the previous version and is now taken by all of the students that need an introductory lab course in Biology.

What kinds of experiments do the students get to do in the class?

Students get exposed to a variety of lab techniques that can be used beyond our classroom. We start with sterile technique and pipetting during the very first week (some students have never pipetted before!). During the first class, the students also spot out some yeast strains so they can start collecting data on the transactivation ability of their p53 mutant right away. Once they have some basic information about the function of their mutant, the students then extract protein from their yeast strains. Throughout the rest of the quarter, students use this protein to conduct a kinetic assay, Western blot, and assess DNA binding ability of their mutant p53. They also get some exposure to fluorescence microscopy when they use a GFP-tagged version of their mutant to determine whether it can localize properly to the nucleus. But the most important thing of all is that students learn how to analyze the data and think critically about it. Not only do they “crunch the numbers” but they must use that information to draw some actual conclusions about what is wrong with their mutant by the end of the quarter.

How hard is it to set up and run the class?

It takes a lot of organization because we have around 200 or more students that take this class every year! Fortunately, we have a great team to help organize the setup of the labs so that the instructors can focus on the teaching. Nicole Bradon manages a small staff that sets up the classrooms and prepares all of the reagents for the lab each week. Dr. Daria Hekmat-Scafe, who is one of the instructors, constructs many of the yeast strains that we give to the students. The team of lecturers (Dr. Shyamala Malladi, Dr. Daria Hekmat-Scafe and I) all work together on lectures and other course materials so everyone gets a similar experience. All together, it takes a lot of behind-the-scenes work, but then the students really get to focus on the experiments and their results.

Do you enjoy teaching the class? What is your favorite part? Your least favorite part?

I love teaching this class! It is so fun to go through this research experience with so many students and they all bring their unique perspectives to the course (we get engineers, psych majors, bio majors, econ majors and others). Also, each section has only 20 students so you really get the chance to get to know them over the course of the 10 weeks. Sometimes the experiments don’t work as planned (like real science) but overall it ends up being a great learning experience.

What do you hope the students will learn and get out of the class? And are they learning/getting it?

We hope that students learn to think critically and what it really means to “think like a scientist”. Too often, science is boiled down to a series of facts that students are expected to memorize and that isn’t what science really is! Science is all about finding exciting questions and constructing experiments that try and answer those questions. The beauty of a research-based lab course is that students can also feel more in charge of their own learning. We have performed assessments of the class and have found that over the course of the quarter, students develop a more sophisticated understanding of what it means to “think like a scientist” and a large portion are more interested in becoming involved in scientific research. I think this is great, as I feel that undergraduate research helped me understand science so much more deeply than many of the courses I had taken.

How would someone at another University go about replicating this course? Are there resources available to help them get started and/or keep it running?

Our group is willing to share our course materials and knowledge with others that are interested in replicating this at other institutions. Anyone who is interested should feel free to contact us! Also, there is a paper in preparation that will describe some of the key aspects of the course as well as more details about what we have learned from the assessments of the course over the past few years.

There you have it…a great class that uses the awesomeness of yeast to teach undergraduates how to think like scientists. Again, if you’re interested in learning more, please contact Tim Stearns and/or Martha Cyert at Stanford.

Imagine you have the instructions for building a car but you don’t know what any of the specific parts do. In other words, you can build a working car but you don’t understand how it works.

If a cell were a car and you removed its radiator, it might adapt by evolving an air cooled system. If it happened soon enough, you might never figure out what the radiator did. Image by Joe Mazzola obtained from Wikimedia Commons.

One way to figure out how the car works would be to remove a part and see what happens. You would then know what role that part played in getting a car to run.

So if you remove the steering wheel, you’d see that the thing runs into a wall. That part must be for steering. When you take out the radiator, the car overheats so that part must be for cooling the engine. And so on.

Sounds like a silly way to figure out how the car works, but this is essentially one of the key ways we try to figure out how a cell works. Instead of parts, we knock out genes and see what happens. A new study by Teng and coworkers is making us rethink this approach.

See, one of the big differences between a machine and a cell is that the cell can react and adapt to the loss of one of its parts. And in fact, it not only can but it almost certainly will.

Each cell has gone through millions of years of evolution to adapt perfectly to its situation. If you tweak that, the cell is going to adapt through mutation of other genes. It is as if we remove the radiator from the car and it evolves an air cooling system like the one in old Volkswagen Bugs.

Teng and coworkers decided to investigate whether or not knocking out a gene causes an organism to adapt in a consistent way. In other words, does removing a gene cause a selection pressure for the same subset of mutations that allows the organism to deal with the loss of the gene. The yeast knockout (YKO) collection, which contains S. cerevisiae strains that individually have complete deletions of each nonessential gene, gave them the perfect opportunity to ask this question.

There have long been anecdotal reports of the YKO strains containing additional, secondary mutations, but the authors first needed to assess this systematically. They came up with an assay that could detect whether secondary mutations were occurring, and if so, whether separate isolates of any given YKO strain would adapt to the loss of that gene in a similar way. The assay they developed had two steps.

The first step was to fish out individual substrains from a culture of yeast that started from a single cell in which a single gene had been knocked out. This was simply done by plating the culture and picking six different, individual colonies. Each colony would have started from a single cell in the original culture.

The second step involved coming up with a way to distinguish differently adapted substrains. The first approach was to see how well each substrain responds to increasing temperatures. To do this, they looked for differences in growth at gradually increasing temperatures using a thermocycler.

They randomly selected 250 YKO strains and found that 105 of them had at least one substrain that reproducibly responded differently from the other substrains in the assay. In contrast, when they looked at 26 isolates of several different wild type strains, including the background strain for the YKO collection, there were no differences between them. This tells us that the variation they saw in the knockout substrains was due to the presence of the original knockout.

So this tells us that strains can pretty quickly develop mutations but it doesn’t tell us that they are necessarily adapting to the knocked out gene. To see if parallel evolution was indeed taking place, the authors chose to look at forty strains in which the same gene was independently knocked out. They found that 26 of these strains that had at least one substrain with the same phenotype, and fifteen of those had mutations that were in the same complementation group. So these 15 strains had evolved in similar ways to adapt to the loss of the same gene.

Teng and coworkers designed a second assay independent of the original heat sensitivity assay and tested a variety of single knockout strains. They obtained similar results that support the idea that knocking out a gene can lead cells to adapt in similar ways. This is both good and bad news.

The bad news is that it makes interpreting knockout experiments a bit trickier. Are we seeing the effect of knocking out the gene or the effect of the secondary mutations that resulted from the knockout? Are we seeing the loss of the radiator in the car or the reshaping that resulted in air cooling? We may need to revisit some earlier conclusions based on knockout phenotypes.

The good news is that not only does this help us to better understand and interpret the results from yeast and mouse (and any other model organism) knockout experiments, it also gives us an insight into evolution and maybe even into the parallel evolution that happens in cancer cells, where mutations frequently co-occur in specific pairs of genes. And while we may never be able to predict if that knock you hear in your engine really needs that $1000 repair your mechanic says it does, we may one day be able to use results like these to predict which cells containing certain mutated genes will go on to cause cancer and which ones won’t.

In the Hunger Games, limited resources mean only the privileged get them. The same is true for methyl groups in yeast and human cells…when in short supply, they are only available to the chosen few. Image by Eva Rinaldi obtained from Wikimedia Commons.

We all know that it’s important to get enough vitamins in our diet. Scary-sounding conditions like scurvy, rickets, and beriberi can all happen when you don’t get enough of them. And that’s not all.

Fairly recently, scientists discovered that when pregnant women get too little folate, their children are at a higher risk for neural tube defects. This connection is so strong that since 1998, the U.S. and Canada have successfully reduced the number of neural tube defects by adding extra folate to grain products.

While these kinds of effects are easy to see, it’s not always so obvious what is going on at the molecular level. But in a new study in GENETICS, Sadhu and coworkers showed that folate and methionine deficiencies can affect us right down to our DNA. And of course, they figured this out by starting with our little friend S. cerevisiae.

Folate and its related compound methionine are pretty important molecules in cellular metabolism. You need folate to make purine nucleotides, and it is essential for keeping just the right levels of methionine in a cell.

And methionine is, of course, one of the essential amino acid building blocks of proteins. But it is more than that. It’s also the precursor for S-adenosyl-methionine (SAM), which provides the methyl groups for protein methylation.

Protein methylation is a big deal for all sorts of things. But one of its most important jobs is undoubtedly controlling levels of gene expression through methylation of histones.

Since folate or methionine deficiency should affect SAM levels, in principle they could affect histone methylation too. But so far this connection had never been shown directly. Sadhu and colleagues set out to see what happens when you deprive S. cerevisiae of these nutrients.

Unlike humans, yeast can synthesize both folate and methionine. So the first step was to make folate- and methionine-requiring strains by deleting the FOL3 or MET2 genes, respectively. These mutant yeast strains couldn’t grow unless they were fed folate or methionine.

Now it was possible to starve these mutant strains by giving them low levels of the nutrients they needed. Starvation for either folate or methionine caused the methylation of a specific lysine residue (K4) of histone H3 to be reduced. Not only that, but expression of specific genes was lower, consistent with their reduced histone methylation.

To see how general this effect was, the authors performed essentially the same experiments in Schizosaccharomyces pombe, which is about as evolutionarily distant from S. cerevisiae as you can get and still be a yeast. In this beast, methionine deficiency also reduced histone methylation. For unknown reasons, folate deficiency didn’t have a significant effect.

Sadhu and coworkers wondered whether this effect was so general that they could even see it in human cells. Since humans are folate and methionine auxotrophs, this experiment was easier to set up. When they grew human cells with starvation levels of folate or methionine, their histone methylation and gene expression were both reduced. So starvation conditions have an impact right down to the level of gene expression, across a wide range of organisms.

The simple explanation for this effect would be that reduced folate leads to reduced SAM levels, and therefore fewer methyl groups are available to modify histones. But the researchers got a surprise when they measured intracellular SAM levels in S. cerevisiae under the starvation conditions: they were the same as in wild type! This conclusion was so surprising that they tried two different, sophisticated methods, but both gave the same result.

They explain this by postulating a kind of metabolic triage. Basically, the cell maintains a certain level of SAM in the cell but there is a pecking order for who gets to use it. At very low nutrient levels, the cell uses the available folate or methionine for the most essential processes such as purine synthesis or translation, and sacrifices histone methylation. As more nutrients become available, then other less critical functions can use them.

This kind of triage might provide an explanation for the link between folate deficiency and neural tube defects, and also for the effectiveness of antifolates against cancer. And it adds to the growing body of evidence that environmental conditions such as famine can have effects that persist across generations. This is an important reminder that any decisions we make today about feeding the hungry could have consequences that reach far into the future.

Wish you were going to Cold Spring Harbor for the Cell Biology of Yeasts meeting this week, November 5-9? SGD will be live tweeting from CSHL, highlighting topics from talks and posters. Keep up with events at the meeting by following @yeastgenome on Twitter or searching #YCB2013 for all tweets!

Folks, yeast has been on a roll lately with regard to helping to understand and finding treatments for human disease. Last week we talked about how synthetic lethal screens may find new, previously unrecognized druggable targets for cancer. And this week it is Parkinson’s disease.

One day, perhaps, most people will marvel at what a huge role yeast had in finding a treatment for Parkinson’s disease. We won’t be at all surprised. Image by Thomas Atilla Lewis.

Now of course yeast can’t get the traditional sort of Parkinson’s disease …it doesn’t have a brain. But it shares enough biology with us that when it expresses a mutant version of α-synuclein (α-syn) that is known to greatly increase a person’s risk for developing Parkinson’s disease, the yeast cell shows many of the same phenotypes as a diseased neuron. The yeast acts as a stand-in for the neuron.

In a new study out in Science, Tardiff and coworkers use this yeast model to identify a heretofore unknown target for Parkinson’s disease in a sort of reverse engineering process. They screened around 190,000 compounds and looked for those that rescued toxicity in this yeast model. They found one significant hit, an N-aryl-benzimidazole (NAB) compound. Working backwards from this hit they identified its target as Rsp5p, a Nedd4 E3 ubiquitin ligase.

The authors then went on to confirm this finding in C. elegans and rat neuron models where this compound halted and even managed to reverse neuronal damage. And for the coup de grace, Chung and coworkers showed in a companion paper that the compound worked in human neurons too. But not just any human neurons.

The authors used two sets of neurons derived from induced pluripotent cells from a single patient. One set of neurons had a mutation in the α-syn gene which is known to put patients at a high risk of Parkinson’s disease-induced dementia. The other set had the mutation corrected. The compound they identified in yeast reversed some of the effects in the neurons with the α-syn mutation without significantly affecting the corrected neurons. Wow.

What makes this even more exciting is that many people thought you couldn’t target α-syn with a small molecule. But as the studies here show, you can target an E3 ubiquitin ligase that can overcome the effects of mutant α-syn. It took an unbiased screen in yeast to reveal a target that would have taken much, much longer to find in human cells.

The mutant α-syn protein ends up in inclusion bodies that disrupt endosomal traffic in the cell. The NAB compound that the authors discovered restored endosomal transport and greatly decreased the numbers of these inclusion bodies. Juicing up Rsp5 seemed to clear out the mutant protein.

The next steps are those usually associated with finding a lead compound—chemical modification to make it safer and more effective, testing in clinical trial and then, if everything goes well, helping patients with Parkinson’s disease. And that may not be all.

The α-syn protein isn’t just involved in Parkinson’s disease. The dementia associated with this protein is part of a larger group of disorders called dementia with Lewy bodies that affects around 1.3 million people in the US. If everything goes according to plan, many of these patients may one day thank yeast for their treatment.

Current cancer treatments are a lot like trying to destroy a particular red plate by letting a bull loose in a china shop. Yes, the plate is eventually smashed, but the collateral damage is pretty severe.

Yeast may help us find ways to treat cancers without all that collateral damage.

Ideally we would want something a bit more discriminating than an enraged bull. We might want an assassin that can fire a single bullet that destroys that red plate.

One way to identify the assassin that can selectively find and destroy cancer cells is by taking advantage of the idea of synthetic lethal mutations. “Synthetic lethal” is a genetic term that sounds a lot more complicated than it really is. Basically the idea is that mutating certain pairs of genes kills a cell, although mutating each gene by itself has little or no effect.

A synthetic lethal strategy seems tailor made for cancer treatments. After all, a big part of what happens when a cell becomes cancerous is that it undergoes a series of mutations. If scientists can find and target these mutated genes’ synthetically lethal partners, then the cancer cell will die but normal cells will not.

This is just what Deshpande and coworkers set out to do in a new study in the journal Cancer Research. They first scanned a previous screen that looked at 5.4 million pairwise interactions in the yeast S. cerevisiae to find the best synthetic lethal pairs. They found 116,000 pairs that significantly affected cell growth only if both genes in the pair were mutated.

A deeper look into the data revealed that 24,000 of these pairs had human orthologs for both genes. In 500 of these pairs, at least one of the partner genes had been shown to be mutated in certain cancers. Using a strict set of criteria (such as the strength and reproducibility of the synthetic lethal effect, and the presence of clear one-to-one orthology between yeast and human), the authors narrowed these 500 down to 21 pairs that they decided to study in mammalian cell lines.

When the authors knocked down the expression of both genes in these 21 gene pairs in a mammalian cell line, they found six that significantly affected growth. They focused the rest of the work on the strongest two pairs, SMARCB1/PMSA4 and ASPSCR1/PSMC2. These mammalian gene pairs correspond to the yeast orthologs SNF5/PRE9 and UBX4/RPT1, respectively.

The authors identified two separate cancer cell lines that harbored mutated versions of the SMARCB1 gene. When this gene’s synthetic lethal partner, PMSA4, was downregulated in these cancer lines, the growth of each cell line was severely compromised. The same was not true for a cell line that had a wild type version of SMARCB1—this cell line was not affected by downregulating PMSA4. The authors used a synthetic lethal screen in yeast to identify a new cancer target which when downregulated selectively killed the cancer without killing “normal” cells.

This proof of principle set of experiments shows how the humble yeast may one day speed up the process of finding cancer treatments without all those nasty side effects (like vomiting, hair loss, anemia and so on). Yeast screens can first be used to identify target genes and then perhaps also to find small molecules that affect the activity of those gene products. Yeast may one day tame the raging bull in a china shop that is current cancer treatments.

A memorial gathering in memory of Fred Sherman will be held at 10:00 am on Friday, December 6, 2013. The gathering will be held in the Ryan Case Methods Room (Rm #1-9576) of the University of Rochester School of Medicine and Dentistry, 601 Elmwood Avenue, Rochester NY 14642. This event will be a celebration of the life and science of Fred, comprised of reminiscences about Fred by some who knew him well, followed by an opportunity for any guest to say a few words about Fred. For more information, contact Mark Dumont (Mark_Dumont@urmc.rochester.edu), Department of Biochemistry and Biophysics, University of Rochester Medical Center, Rochester, NY 14642 (Phone 585-275-2466).

The glass slipper screen couldn’t find the hidden glamour of the prefoldin complex. But the GLAM assay did.

The prefoldin complex seemed like an ordinary housekeeper. It sat in the cytoplasm and folded protein after protein, just as Cinderella spent her days folding laundry for her stepsisters.

In the old story, the handsome prince searched the kingdom for a girl whose foot would fit the glass slipper. Using this crude screen, he finally found Cinderella and revealed her to be the true princess that she was.

In a new study, Millán-Zambrano and coworkers did essentially the same thing for the prefoldin complex. They searched the genome of S. cerevisiae for new mutations that would affect transcription elongation. They found the prefoldin complex subunit PFD1 and went on to establish that in addition to its humdrum cytoplasmic role, prefoldin has a surprising and glamorous role in the nucleus facilitating transcriptional elongation.

The researchers decided to cast a wide net in their search for genes with previously undiscovered roles in transcriptional elongation. Their group had already worked out the GLAM assay (Gene Length-dependent Accumulation of mRNA), which can uncover elongation defects.

The assay uses two different reporter gene constructs that both encode Pho5p, an acid phosphatase. One generates an mRNA of average length, while the other generates an unusually long mRNA when fully transcribed. The acid phosphatase activity of Pho5p is simple to measure, and correlates well with abundance of its mRNA. If there is a problem with transcriptional elongation in a particular mutant strain, there will be much less phosphatase activity generated from the longer form than from the shorter one. So the ratio of the two gives a good indication of how well elongation is working in that mutant strain.

Millán-Zambrano and coworkers used this assay to screen the genome-wide collection of viable deletion mutants. They came up with mutations in lots of genes that were already known to affect transcriptional elongation, confirming that the assay was working. They also found some genes that hadn’t been shown to be involved in elongation before. One of these was PFD1, a gene encoding a subunit of the prefoldin complex. As this deletion had one of the most significant effects on elongation, they decided to investigate it further.

Prefoldin is a non-essential complex made of six subunits that helps to fold proteins in the cytoplasm as they are translated. The authors tested mutants lacking the other subunits and found that most of them also had transcriptional elongation defects in the GLAM assay, although none quite as strong as the pfd1 mutant.

Since prefoldin is important in folding microtubules and actin filaments, the researchers wondered whether the GLAM assay result was the indirect effect of cytoskeletal defects. They were able to rule this out by showing that drugs that destabilize the cytoskeleton didn’t affect the GLAM ratio in wild-type cells, and that mutations in prefoldin subunits didn’t confer strong sensitivity to those drugs.

If prefoldin has a role in transcription, it would obviously need to get inside the nucleus. It had previously been seen in the cytoplasm, but when the authors took another look, they found it in the nucleus as well. Furthermore, Pfd1p was bound to the chromatin of actively transcribed genes! And besides its effect on transcription elongation, the pfd1 mutant has lower levels of RNA polymerase II occupancy and abnormal patterns of histone binding on transcribed genes.

There’s still a lot of work to be done to figure out exactly what prefoldin is doing during transcriptional elongation. Right now, the evidence points to its involvement in evicting histones from genes in order to expose them for transcription. But even before all the details of this story are worked out, this is a good reminder never to assume that an everyday housekeeper is only that.

With the right screen we can find new and exciting things about the most humdrum of characters. A glass slipper screen revealed the princess under that apron and chimney soot. And a GLAM assay revealed the sexy, exciting transcription elongation factor that is prefoldin.

Separating the wheat from the chaff is a lot easier than figuring out which variants matter in a GWAS.

Cheap and easy genome sequencing has been both a blessing and a curse. We are able to find an incredible wealth of variation, but for the most part we have no easy way to tell whether a difference might contribute to a disease or not.

The poster child for this problem is autism. Lots of genome wide association studies (GWAS) have been done and lots of rare variants in lots of different genes have been found – unfortunately, way too many to pick out the ones that really matter.

Luckily our friend yeast can help. Various researchers have identified a number of variants in the human cation/proton antiporter gene NHE9 that associate with autism. In a new study, Kondapalli and coworkers used the NHE9 ortholog NHX1 from S. cerevisiae as an initial screen to identify which variants impact the activity of the NHE9 protein. They found that two of the three mutations they looked at compromised the activity of yeast Nhx1p.

They then set out to confirm these results in mammalian cells. When they looked at protein activity in glial cells, they found that all three mutations compromised the activity of NHE9. This is obviously different from what they found in yeast.

Now this doesn’t mean that yeast is useless for this approach (God forbid!). No, instead it means that it is probably only useful for a subset of autism mutations. Kondapalli and coworkers had suspected this, but apparently the subset is smaller than they initially thought.

The first thing they did was to generate a rough three dimensional map of the NHE9 protein in order to see which parts the two proteins shared. The idea is that they could then do a quick screen in yeast with mutations that affect the shared structure.

While the structure of NHE9 has not been solved, we do have the structure of its distant bacterial relative, NhaA. Kondapalli and coworkers aligned the two along with the yeast ortholog Nhx1p and identified conserved regions.

Three of the NHE9 mutations associated with autism—V176I, L236S, and S438P—were all predicted to be in shared, membrane-spanning parts of the protein. The researchers introduced the equivalent mutations into NHX1—V167I, I222S, and A438P.

A yeast deleted for NHX1 grows poorly in high salt and low pH and also has increased sensitivity to hygromycin B, as compared to a yeast with a functioning NHX1. Two of the mutant genes, carrying A438P or I222S, failed to rescue these growth defects. The other mutant gene, with the V167I change, worked as well as wild type NHX1 at rescuing the yeast. So at least in yeast, two of the three mutations appear to impact protein activity.

The next step was to see if the same was true in mammals. Easier said than done! Ideally they would want to investigate whether these mutations affected the protein in the cells where NHE9 is usually active. Too bad no one knows this protein’s natural habitat. This is why the researchers starting slicing mouse brains to figure out when and where the protein is expressed.

While we don’t have time or space to go into all the details here, Kondapalli and coworkers found that when and where in the brain NHE9 was expressed made sense as far as a possible contribution to autism. They also found that glial cells had about 1.2 fold more NHE9 transcripts than did neuronal cells. They therefore did their assays of protein activity in a type of glial cells called astrocytes.

While they couldn’t completely knock out NHE9 in mouse astrocytes, they were able to knock down its expression by over 80%. When they added back the mutant NHE9 genes, they found that all three failed to mimic the effect of adding back wild type NHE9 to these cells. This is different than what they found in yeast, where only two of the mutations impacted protein activity.

When they went back to their 3D model, they saw that the mutation that differed, V167I, affected a less defined part of the structure. This points to the fact that for the quick yeast screen to work, they need to be looking at parts of the protein where the structure is shared between the yeast and the human version. In a perfect world they would have had crystal structures of each to work off of instead of having to kludge together a model.

In any event, this is the first step towards validating yeast as a quick screen for identifying mutations that can impact protein activity and so are good candidates for being involved in disease. Yeast may help scientists separate the wheat from the chaff of GWAS and so help figure out how diseases happen and maybe help find treatments or even cures. Well done yeast.

Congratulations to Randy Schekman, James Rothman, and Thomas Südhof, who have been awarded the 2013 Nobel Prize in Physiology or Medicine for their work in understanding how the cell organizes its transport system. Randy Schekman used the awesome power of yeast to identify and characterize genes required for vesicle traffic. James Rothman characterized these and other proteins in mammalian cells, and Thomas Südhof showed the critical role of vesicle trafficking in nerve cells. You can read summaries of their Nobel winning work at Nature, The Scientist, and The New York Times, or search SGD to see how each of these researchers has used our model organism in their research: Randy Schekman, James Rothman, Thomas Südhof.

Remember in Dune when Paul Muad’Dib took a sip of the “Water of Life” and needed weeks in a coma to turn it into something that let him survive and emerge even more powerful than before? Turns out yeast sometimes have to do something similar.

Now of course the yeast aren’t consciously moving molecules around to deal with a poison like Paul did. No, instead they sometimes need to transcribe low levels of a mutated gene over a long period of time to survive in a new environment.

This process is called retromutagenesis. The idea is that a cell gets a mutation that would allow it to survive and prosper in a new environment if only it could replicate its DNA. Unfortunately the new environment is so unforgiving that the cell can’t replicate.

The cell escapes this catch-22 by transcribing the gene with the mutation so that the mutant protein can get made. Once enough of this protein is made, the cell manages to get up enough steam to power through a cell cycle. Now the mutation is established and the yeast can make lots of mutant protein and happily chug along.

In a new study in the latest issue of GENETICS, Shockley and coworkers hypothesize that something like this is happening in their experiments. They were studying oxidative damage to DNA and found that some of their mutants required many days before they could grow in the absence of tryptophan (trp). They argue that these late arising revertants were due to the cells having to wait until retromutagenesis allowed enough functional Trp5p to be made so the cell could replicate.

The authors have created strains of yeast with various mutations in the TRP5 gene that cause the yeast to be unable to grow in the absence of trp. What makes these strains so useful is that they are set up in such a way that six different, specific point reversions can result in a functional TRP5 gene. They can then analyze any Trp+ revertants to see what types of damage lead to which type of mutations.

One of the first things the authors discovered was that oxidative damage caused all six different reversions. While this was interesting, the specific mutation they wanted to focus on was a G to T transversion which occurs when G is converted to 8-oxoguanine. This is why they focused on the trp5-A149C strain.

The main way that yeast cells deal with 8-oxoguanine is by removing it with the Ogg1 protein, a DNA glycosylase. When Shockley and coworkers deleted this gene in their strain, the number of revertants increased by 20-fold. From this they concluded that most of the revertants were the result of the misreplication of an 8-oxoguanine.

This is where the yeast run into a problem. In the absence of trp, the trp5 mutants do not replicate at all…they do not go through even one cell cycle. But to revert to a functional TRP5 gene, this strain needs to go through a cell cycle. This is why the authors think that the first step towards reversion is a mutation in the TRP5 transcript.

Consistent with this idea is the fact that the mutated G in this strain is on the transcribed strand and that this is important for high revertant frequencies. It also helps to explain why revertants took so long to appear. Basically there had to be a buildup of enough functional Trp5p to allow a single cell cycle to happen. Then the G could be converted to a T and the yeast could happily grow. In this specialized case, it looks like reversion is dependent on retromutagenesis.

But retromutagenesis, also called transcriptional mutagenesis, doesn’t happen only in yeast cells. It’s being studied as a possible way that all kinds of quiescent cells, such those in the process of becoming tumor cells, or bacteria whose growth has been stopped by an antibiotic, can mutate and escape the conditions that are restricting them. Our little friend may not save the human race from destruction like Paul did, but once again yeast is proving pretty darn useful in getting results that make a difference for human health.

Yeast has been responsible for a lot of hook ups in its day (think beer goggles and margaritas on the beach). Now it is payback time. In a new study, Giraldo-Perez and Goddard have figured out how to make yeast more promiscuous.

If he were a yeast, he’d harbor the VDE homing endonuclease.

No, they don’t get the yeast drunk. Instead, they found that strains containing VDE, a homing endonuclease gene (HEG), entered meiosis more often than genetically identical strains that lacked VDE. The yeast that contained this “selfish” gene (well, actually intein) were ready to go haploid more often than those that didn’t.

VDE and its ilk are said to be selfish because they end up getting passed down to more offspring than a certain Austrian monk might have predicted. When a diploid is heterozygous for an HEG, the homing endonuclease cuts the sister chromosome at the equivalent spot. Then, when the diploid undergoes meiosis, the sister is repaired through recombination causing both chromosomes to contain the VDE gene. Now instead of two spores containing VDE, all four will.

Giraldo-Perez and Goddard monitored the percentage of sporulating cells over a 30 day period and found that after five days, a higher percentage of diploids homozygous for VDE sporulated compared to diploids heterozygous for or lacking VDE. The authors contend that under the right conditions, this increased sporulation would allow VDE to spread through a population 20 times faster than it might otherwise. And the authors found that VDE needs something like this or it might disappear.

Like alcohol, VDE isn’t all lowered inhibitions and good times. For example, yeast homozygous for VDE grow significantly more slowly than do yeast lacking VDE in YPD, grape juice, vineyard soil, vine bark (heterozygotes fall in between). This obviously puts yeast carrying VDE at a disadvantage, meaning that if it didn’t have another trick up its sleeve, it would dwindle away to nothing. That trick is speeding up sporulation.

The authors weren’t able to determine why this little bit of DNA can have such a profound effect on the growth rate of yeast. It is almost certainly too little DNA to affect the time it takes the yeast to copy its DNA. And the endonuclease itself is probably not randomly nicking the chromosomal DNA in the mitotic state, since it is kept out of the nucleus by host encoded karyopherins.

So VDE is a truly a parasitic selfish gene. It is parasitic because it sucks a little of the life out of a yeast cell. And it is selfish because way more daughters end up with it than might be predicted. Sounds like a nice description for many drunk people…

Back in 2008 and 2011 there were huge spikes in the cost of food that caused riots in various parts of the world. These things were pretty bad and one of our favorite beast’s best products, ethanol, may have been at least partly to blame. In an attempt to deal with global warming, governments had created incentives that made it more lucrative to turn food into ethanol to power cars rather than keeping it as food to feed people. The law of unintended consequences reared its ugly head and caused food prices to rise high enough to be unaffordable by the very poor.

Getting yeast to turn more of this into ethanol is good for us and the environment.

This situation arose because right now, pretty much the only commercially viable way to make ethanol is to use sugars like those found in sugar cane or starches like those found in corn. Ultimately this won’t be a problem once scientists learn to coax yeast or other microorganisms to make ethanol out of agricultural waste. Until then, though, one way to lessen the impact of ethanol production on food supplies might be to engineer a yeast strain that can more efficiently turn sugars into ethanol.

One of the most inefficient parts of yeast fermentation is that the silly thing converts anywhere from 4-10% of the sugars it gets into glycerol instead of ethanol. In a new study, Guadalupe-Medina and coworkers have engineered a strain of yeast that produces 60% less glycerol and 8% more ethanol than other commercial strains. If they can scale this up, it might help us feed both the world’s population and our cars.

It has been known for some time that yeast end up making glycerol during fermentation because of redox-cofactor balancing issues. In essence, the excess NADH that is made in fermentation reactions is reoxidized by converting part of the sugar into glycerol. One obvious way to get less glycerol would be to give the yeast some other way to reoxidize its NADH.

Guadalupe-Medina and coworkers decided to persuade yeast to use carbon dioxide instead of sugars. Not only would this make sugar use more efficient, but their particular plan would also convert that carbon dioxide into a precursor that could be shunted into the ethanol producing pathway. Theoretically the yeast should now increase its ethanol production both by wasting less sugar on glycerol and by turning carbon dioxide into ethanol. And it turns out that this idea actually worked in practice.

The first step was to introduce the Rubisco enzyme into the yeast. Rubisco (ribulose-1,5-bisphosphate carboxylase oxygenase) is really one of the key enzymes in life…it provides the foundation for almost all life on the planet by fixing carbon dioxide from the air into ribulose-1,5 phosphate. But that isn’t the important point here. No, the key point for this work is that in the process of doing this, the enzyme oxidizes NADH. By putting Rubisco in yeast, the yeast should now be able to reoxidize its NADH without making useless glycerol.

Of course this is easier said than done! Rubisco is multi-subunit in most beasts and persnickety to boot. But with a bit of work, they managed to get Saccharomyces cerevisiae to express a working copy of Rubisco.

So they would only have to introduce a single gene, the authors used the single subunit enzyme from T. dentrificans. As expected, this gene alone was not enough. They knew from previous work that Rubisco would not work in yeast without the help of a couple of E. coli chaperones, groEL and groES. When they expressed all three genes at the same time, they got Rubisco to fix carbon dioxide in Saccharomyces cerevisiae.

The next step was to introduce the enzyme phosphoribulokinase (PRK) so that the ribulose-1,5 phosphate could be converted into 3-phosphoglycerate, a precursor in the ethanol pathway. Luckily this was much easier than Rubisco and worked on the first try. They had now engineered a Frankenyeast that should be able to make more ethanol and less glycerol.

When they tested the new strain, Guadalupe-Medina and coworkers found they had indeed engineered a more efficient yeast. In anaerobic chemostat conditions, this yeast made 68% less glycerol and 11% more ethanol than the usual commercial strain. They obtained similar results, 60% less glycerol and 8% more ethanol, in batch fermentations. They had succeeded in improving an already awesome beast.

If this strain works on an industrial scale and if commercial producers all used this strain instead of the ones they currently use, the authors calculate we could get an extra 5 billion liters of ethanol added to the 110 billion we are already making. That might just be enough to tide us over until scientists come up with a way to make ethanol commercially from non-food sources.

The yeast community mourns the loss of Dr. Fred Sherman, who passed away on September 16, 2013. Dr. Sherman was a member of the faculty at the University of Rochester from 1961 until his death. He served as Chair of the Department of Biochemistry and then Chair of the combined Department of Biochemistry and Biophysics from 1982-1999. He performed ground-breaking research on the structure and regulation of genes and the effects of genetic mutations on proteins and was a proponent of the use of baker’s yeast as a genetic model system – a system that is now used at virtually all research centers worldwide, largely due to Dr. Sherman’s efforts and his teaching of many leaders in the field. The importance of his work has been recognized by his appointment to the prestigious National Academy of Sciences in 1985, by his receipt of an Honorary Doctorate from the University of Minnesota in 2002, by his election as a Fellow of the American Association for the Advancement of Science in 2006, and by his receipt of both the George W. Beadle Award and the Lifetime Achievement Award from the Genetics Society of America in 2006. He was continuously funded by NIH for over 45 years.

Dr. Sherman’s family will receive friends on FRIDAY September 20, from 3-7 PM at Michael R. Yackiw Funeral Home, 1650 Empire Blvd., Webster. On SATURDAY, friends may join his family for a graveside service gathering at the Mt. Hope Ave. entrance of Mt. Hope Cemetery at 11 AM. In lieu of flowers, contributions may be directed to a fund to support an annual lecture in Fred’s memory. To donate please mail donations to: Fred Sherman Lecture of the University of Rochester, Box 712, University of Rochester Medical Center, 601 Elmwood Ave., Rochester NY, 14642.

Plans for a future memorial service will be announced at a later date.